{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "LDA_final_version.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vo4p1hr9q5qP",
        "colab_type": "text"
      },
      "source": [
        "# Install"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "G23XYD4ZcAiL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 479
        },
        "outputId": "d03adb9e-5d87-44ce-fac5-e9343908955f"
      },
      "source": [
        "!pip install unidecode\n",
        "!pip install langdetect\n",
        "!pip install langid\n",
        "!pip install pyldavis"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: unidecode in /usr/local/lib/python3.6/dist-packages (1.1.1)\n",
            "Requirement already satisfied: langdetect in /usr/local/lib/python3.6/dist-packages (1.0.8)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from langdetect) (1.12.0)\n",
            "Requirement already satisfied: langid in /usr/local/lib/python3.6/dist-packages (1.1.6)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.6/dist-packages (from langid) (1.18.5)\n",
            "Requirement already satisfied: pyldavis in /usr/local/lib/python3.6/dist-packages (2.1.2)\n",
            "Requirement already satisfied: scipy>=0.18.0 in /usr/local/lib/python3.6/dist-packages (from pyldavis) (1.4.1)\n",
            "Requirement already satisfied: pandas>=0.17.0 in /usr/local/lib/python3.6/dist-packages (from pyldavis) (1.0.5)\n",
            "Requirement already satisfied: wheel>=0.23.0 in /usr/local/lib/python3.6/dist-packages (from pyldavis) (0.34.2)\n",
            "Requirement already satisfied: numexpr in /usr/local/lib/python3.6/dist-packages (from pyldavis) (2.7.1)\n",
            "Requirement already satisfied: numpy>=1.9.2 in /usr/local/lib/python3.6/dist-packages (from pyldavis) (1.18.5)\n",
            "Requirement already satisfied: pytest in /usr/local/lib/python3.6/dist-packages (from pyldavis) (3.6.4)\n",
            "Requirement already satisfied: funcy in /usr/local/lib/python3.6/dist-packages (from pyldavis) (1.14)\n",
            "Requirement already satisfied: future in /usr/local/lib/python3.6/dist-packages (from pyldavis) (0.16.0)\n",
            "Requirement already satisfied: jinja2>=2.7.2 in /usr/local/lib/python3.6/dist-packages (from pyldavis) (2.11.2)\n",
            "Requirement already satisfied: joblib>=0.8.4 in /usr/local/lib/python3.6/dist-packages (from pyldavis) (0.15.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.17.0->pyldavis) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.6/dist-packages (from pandas>=0.17.0->pyldavis) (2.8.1)\n",
            "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.6/dist-packages (from pytest->pyldavis) (19.3.0)\n",
            "Requirement already satisfied: py>=1.5.0 in /usr/local/lib/python3.6/dist-packages (from pytest->pyldavis) (1.8.2)\n",
            "Requirement already satisfied: more-itertools>=4.0.0 in /usr/local/lib/python3.6/dist-packages (from pytest->pyldavis) (8.4.0)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from pytest->pyldavis) (1.12.0)\n",
            "Requirement already satisfied: atomicwrites>=1.0 in /usr/local/lib/python3.6/dist-packages (from pytest->pyldavis) (1.4.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.6/dist-packages (from pytest->pyldavis) (47.3.1)\n",
            "Requirement already satisfied: pluggy<0.8,>=0.5 in /usr/local/lib/python3.6/dist-packages (from pytest->pyldavis) (0.7.1)\n",
            "Requirement already satisfied: MarkupSafe>=0.23 in /usr/local/lib/python3.6/dist-packages (from jinja2>=2.7.2->pyldavis) (1.1.1)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gf7VEDIMucFo",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 170
        },
        "outputId": "8f3b0921-11ea-4117-d4b2-57d20c1df2ad"
      },
      "source": [
        "import nltk\n",
        "\n",
        "nltk.download('stopwords')\n",
        "nltk.download('rslp')\n",
        "nltk.download('punkt')\n",
        "nltk.download('wordnet')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[nltk_data] Downloading package stopwords to /root/nltk_data...\n",
            "[nltk_data]   Package stopwords is already up-to-date!\n",
            "[nltk_data] Downloading package rslp to /root/nltk_data...\n",
            "[nltk_data]   Package rslp is already up-to-date!\n",
            "[nltk_data] Downloading package punkt to /root/nltk_data...\n",
            "[nltk_data]   Package punkt is already up-to-date!\n",
            "[nltk_data] Downloading package wordnet to /root/nltk_data...\n",
            "[nltk_data]   Package wordnet is already up-to-date!\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HYsA3V6brIgR",
        "colab_type": "text"
      },
      "source": [
        "# Imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y3VaYPBxNJuT",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import csv\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sb\n",
        "import re\n",
        "import os\n",
        "import joblib\n",
        "\n",
        "from scipy import stats\n",
        "from scipy import sparse\n",
        "import scipy.cluster.hierarchy as sch\n",
        "import scipy.spatial.distance as scdist\n",
        "\n",
        "from IPython.display import display, HTML\n",
        "import statsmodels.sandbox.stats.multicomp as mc\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.patches as mpatches\n",
        "\n",
        "from sklearn.pipeline import make_pipeline\n",
        "from sklearn.preprocessing import Normalizer\n",
        "from sklearn.decomposition import TruncatedSVD\n",
        "from sklearn.manifold import TSNE\n",
        "from sklearn.cluster import KMeans\n",
        "from sklearn.metrics.pairwise import cosine_similarity\n",
        "from sklearn.feature_extraction.text import TfidfTransformer\n",
        "from sklearn.metrics import silhouette_score, silhouette_samples\n",
        "from sklearn.feature_extraction.text import TfidfVectorizer\n",
        "from sklearn.feature_extraction.text import CountVectorizer\n",
        "\n",
        "from textblob import TextBlob\n",
        "import collections\n",
        "import unidecode\n",
        "import langid\n",
        "import seaborn as sns\n",
        "\n",
        "from bs4 import BeautifulSoup\n",
        "from langdetect import detect, detect_langs\n",
        "\n",
        "from nltk.corpus import stopwords # Import the stop word list\n",
        "from nltk.stem import RSLPStemmer\n",
        "from nltk.stem import WordNetLemmatizer\n",
        "from nltk.corpus import wordnet\n",
        "\n",
        "# Import the wordcloud library\n",
        "from wordcloud import WordCloud\n",
        "\n",
        "import warnings\n",
        "warnings.simplefilter(\"ignore\", DeprecationWarning)\n",
        "\n",
        "# Load the LDA model from sk-learn\n",
        "from sklearn.decomposition import LatentDirichletAllocation as LDA\n",
        "\n",
        "# from pyLDAvis import sklearn as sklearn_lda\n",
        "import pickle \n",
        "\n",
        "import pyLDAvis\n",
        "import pyLDAvis.sklearn\n",
        "\n",
        "pyLDAvis.enable_notebook()\n",
        "\n",
        "sns.set_style('whitegrid')\n",
        "sb.set_style(\"whitegrid\", {'axes.grid' : False})\n",
        "%matplotlib inline\n",
        "\n",
        "lemmatizer = WordNetLemmatizer()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PL0qir8mcFtp",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b95b5719-e0ba-4913-a87a-28f685a51442"
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZEa-ecTsuwAm",
        "colab_type": "text"
      },
      "source": [
        "# Functions"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "8CkhJTMVu7Mz",
        "colab_type": "text"
      },
      "source": [
        "**Stopwords**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XQUEEZe1uziS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "b37afd27-9d66-4cad-82f1-3c7b3919bd5c"
      },
      "source": [
        "file_stops = open('/content/drive/My Drive/covid-es-shared/notebooks/files/stop_words.txt')\n",
        "file_stops = file_stops.read()\n",
        "stops = file_stops.split()\n",
        "\n",
        "# Stopwords personlized\n",
        "stops_corpus = ['covid', 'covid19', 'covid-19', 'corona', 'coronavirus', 'coronovírus', 'nconv', 'virus', 'pandemic', 'https', 'print', 'array', 'input']\n",
        "\n",
        "stops = set(stops + stops_corpus)\n",
        "\n",
        "print(\"Stopwords:\", len(stops))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Stopwords: 832\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ro4fVEBlvlrq",
        "colab_type": "text"
      },
      "source": [
        "**Constants**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LH1vpAZJvlI5",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "min_length = 3"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OIhlaGxZu2ms",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "# Detect English text\n",
        "\n",
        "def is_en(text):\n",
        "  #lang = TextBlob(para)  \n",
        "  #i1   = lang.detect_language()\n",
        "  i2   = detect(text)\n",
        "  i3   = langid.classify(text)[0]\n",
        "\n",
        "  return i2 == \"en\" or i3 == \"en\" # i1 == \"en\" or"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FKXWM7E5vgpM",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def remove_tag_code(html_text):\n",
        "  soup = BeautifulSoup(html_text, \"lxml\")\n",
        "  codes = soup(\"code\")\n",
        "  for code in codes:\n",
        "    code.decompose()\n",
        "  return soup.text\n",
        "  \n",
        "def textblob_tokenizer_text(str_input):\n",
        "    letters_only = re.sub(u'[^a-záéíóúâêîôãõç ]', ' ', str_input)\n",
        "    tokens = nltk.word_tokenize(letters_only)\n",
        "    tokens = [unidecode.unidecode(token) for token in tokens if not token in stops and len(token) >= min_length]\n",
        "    tokens = [lemmatizer.lemmatize(token) for token in tokens]   \n",
        "    text = ' '.join(tokens)\n",
        "\n",
        "    if len(text) > min_length:\n",
        "      return text\n",
        "\n",
        "    return \"\"\n",
        "\n",
        "def textblob_tokenizer_html(str_input):\n",
        "    return textblob_tokenizer_text(remove_tag_code(str_input))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AZ7vWoPtvgin",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def generate_cloud(df, column_text_processed, background_color=\"white\", max_words=5000, contour_width=3, contour_color='steelblue'):\n",
        "  # Join the different processed titles together.\n",
        "  long_string = ','.join(list(df[column_text_processed].values))\n",
        "  # Create a WordCloud object\n",
        "  wordcloud = WordCloud(background_color=background_color, max_words=max_words, contour_width=contour_width, contour_color=contour_color)\n",
        "  # Generate a word cloud\n",
        "  wordcloud.generate(long_string)\n",
        "  # Visualize the word cloud\n",
        "  # wordcloud.to_image()\n",
        "\n",
        "  plt.figure()\n",
        "  plt.imshow(wordcloud, interpolation=\"bilinear\")\n",
        "  plt.axis(\"off\")\n",
        "  plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yrFmLqC_u2g_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def plot_10_most_common_words(count_data, count_vectorizer):\n",
        "    words = count_vectorizer.get_feature_names()\n",
        "    total_counts = np.zeros(len(words))\n",
        "    \n",
        "    for t in count_data:\n",
        "        total_counts+=t.toarray()[0]\n",
        "    \n",
        "    count_dict = (zip(words, total_counts))\n",
        "    count_dict = sorted(count_dict, key=lambda x:x[1], reverse=True)[0:10]\n",
        "    words = [w[0] for w in count_dict]\n",
        "    counts = [w[1] for w in count_dict]\n",
        "    x_pos = np.arange(len(words)) \n",
        "    \n",
        "    plt.figure(2, figsize=(15, 15/1.6180))\n",
        "    plt.subplot(title='10 most common words')\n",
        "    sns.set_context(\"notebook\", font_scale=1.25, rc={\"lines.linewidth\": 2.5})\n",
        "    sns.barplot(x_pos, counts, palette='husl')\n",
        "    plt.xticks(x_pos, words, rotation=90) \n",
        "    plt.xlabel('words')\n",
        "    plt.ylabel('counts')\n",
        "    plt.show()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UQJuStcrzMiq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def preprocessing(df, column_text, column_text_processed=\"processed\", is_html=False, check_language=False, min_df=5, max_df=0.8, ngram_range=(1, 1)):\n",
        "  \n",
        "  print(\"Load dataframe shape: \", df.shape)\n",
        "\n",
        "  df = df.dropna(subset=[column_text])\n",
        "  df = df[ (df[column_text].notnull()) & (df[column_text]!=u'') ]\n",
        "\n",
        "  print(\"After removing invalid values:\", df.shape)\n",
        "  \n",
        "  texts = df[column_text].str.lower()\n",
        "\n",
        "  if is_html:\n",
        "    df[column_text_processed] = [textblob_tokenizer_html(text) for text in texts]\n",
        "  else:\n",
        "    df[column_text_processed] = [textblob_tokenizer_text(text) for text in texts]\n",
        "\n",
        "  df = df.dropna(subset=[column_text_processed])\n",
        "  df = df[ (df[column_text_processed].notnull()) & (df[column_text_processed]!=u'') ]\n",
        "\n",
        "  print(\"After removing blank values in text processing: \", df.shape)\n",
        "\n",
        "  df['ID'] = range(df.shape[0])\n",
        "  df.reset_index(drop=True, inplace=True)\n",
        "\n",
        "  if check_language:\n",
        "    drop_list = []\n",
        "\n",
        "    # Remove unwritten text in English\n",
        "    for index in df.index:\n",
        "      text = (df[column_text_processed])[index]\n",
        "      if not is_en(text):\n",
        "        # print(index, \" >>> \", text)\n",
        "        drop_list.append(index)\n",
        "    \n",
        "    df.drop(df.index[drop_list], inplace=True)\n",
        "\n",
        "    print(\"After removing unwritten text in English:\", df.shape)\n",
        "\n",
        "  # Initialise the count vectorizer with the English stop words\n",
        "  count_vectorizer = CountVectorizer(stop_words='english', min_df=min_df, max_df=max_df, ngram_range=ngram_range)\n",
        "\n",
        "  # Fit and transform the processed titles\n",
        "  count_data = count_vectorizer.fit_transform(df[column_text_processed])\n",
        "\n",
        "  tfidf_vectorizer = TfidfVectorizer(**count_vectorizer.get_params())\n",
        "  dtm_tfidf = tfidf_vectorizer.fit_transform(df[column_text_processed])\n",
        "\n",
        "  return count_vectorizer, count_data, tfidf_vectorizer, dtm_tfidf, df, column_text_processed"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "15fhQn87wWd9",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def generate_lda(data, number_topics):\n",
        "  # Create and fit the LDA model\n",
        "  lda = LDA(n_components=number_topics, n_jobs=-1)\n",
        "  lda.fit(data)\n",
        "\n",
        "  return lda"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "68s0rS2DCK3W",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def add_topics(df, lda, data):\n",
        "  results = lda.transform(data)\n",
        "  num_topics = len(results[0])\n",
        "\n",
        "  topics_names = [\"topic_\" + str(topic) for topic in range(1, num_topics + 1)]\n",
        "  topic_encoded_df = pd.DataFrame(results, columns=topics_names)\n",
        "  df[topics_names] = pd.DataFrame(results, index=df.index)\n",
        "\n",
        "  topics = []\n",
        "\n",
        "  for topic in results:\n",
        "    max_value = max(topic)\n",
        "    i, = np.where(np.isclose(topic, max_value))\n",
        "    topics.append(i[0] + 1)\n",
        "\n",
        "  df['topic'] = topics\n",
        "\n",
        "  return df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "j_4t-OGo5W5Z",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def generate_topics(df, column_text, column_text_processed, is_html, number_topics, with_tfidf=True, check_language=False):\n",
        "\n",
        "  count_vectorizer, count_data, tfidf_vectorizer, dtm_tfidf, df, _ = preprocessing(df, column_text, column_text_processed=column_text_processed, is_html=is_html, check_language=check_language)\n",
        "\n",
        "  #Save vectorizer.vocabulary_\n",
        "  pickle.dump(count_vectorizer.vocabulary_,open(\"count_vectorizer.pkl\",\"wb\"))\n",
        "  pickle.dump(tfidf_vectorizer.vocabulary_,open(\"tfidf_vectorizer.pkl\",\"wb\"))\n",
        "\n",
        "  generate_cloud(df, column_text_processed)\n",
        "  plot_10_most_common_words(count_data, count_vectorizer)\n",
        "\n",
        "  if with_tfidf:\n",
        "    lda = generate_lda(dtm_tfidf, number_topics)\n",
        "    joblib.dump(lda, \"lda_tfidf_{}_model.jl\".format(number_topics))\n",
        "    preparedData = pyLDAvis.sklearn.prepare(lda, dtm_tfidf, tfidf_vectorizer)\n",
        "    pyLDAvis.save_html(preparedData, \"topics_{}_tfidf.html\".format(number_topics))\n",
        "    df = add_topics(df, lda, dtm_tfidf)\n",
        "  else:\n",
        "    lda = generate_lda(count_data, number_topics)\n",
        "    joblib.dump(lda, \"lda_count_{}_model.jl\".format(number_topics))\n",
        "    preparedData = pyLDAvis.sklearn.prepare(lda, count_data, count_vectorizer)\n",
        "    pyLDAvis.save_html(preparedData, \"topics_{}_count.html\".format(number_topics))\n",
        "    df = add_topics(df, lda, count_data)\n",
        "\n",
        "  return preparedData, df"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Fqh8af2s818G",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def load_count_vectorizer(file_name):\n",
        "  return CountVectorizer(vocabulary=pickle.load(open(file_name, \"rb\")))\n",
        "\n",
        "def load_tfidf_vectorizer(file_name):\n",
        "  return TfidfVectorizer(vocabulary=pickle.load(open(file_name, \"rb\")))\n",
        "\n",
        "def load_lda(file_name):\n",
        "  return joblib.load(file_name)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qorTeNPg96Zw",
        "colab_type": "text"
      },
      "source": [
        "# Stack Overflow"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uMfvIwIk-lJL",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 728
        },
        "outputId": "9add78d9-b72f-4ef4-9ee1-a17a3a58847a"
      },
      "source": [
        "number_topics = 7\n",
        "column_text = 'Body'\n",
        "column_text_processed = 'body_processed'\n",
        "is_html = True\n",
        "with_tfidf = True\n",
        "check_language = False # Check manual\n",
        "\n",
        "# Load dataframe\n",
        "df = pd.read_csv('/content/drive/My Drive/covid-es-shared/database/raw_text_selected.csv', sep=',', quoting=csv.QUOTE_ALL)\n",
        "\n",
        "preparedData, df = generate_topics(df, column_text, column_text_processed, is_html, number_topics, with_tfidf, check_language)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Load dataframe shape:  (1190, 2)\n",
            "After removing invalid values: (1190, 2)\n",
            "After removing blank values in text processing:  (1189, 3)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/feature_extraction/text.py:1817: UserWarning: Only (<class 'numpy.float64'>, <class 'numpy.float32'>, <class 'numpy.float16'>) 'dtype' should be used. <class 'numpy.int64'> 'dtype' will be converted to np.float64.\n",
            "  UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x667.491 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2Ju0_gRKClMQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 881
        },
        "outputId": "ec21a02a-5378-4e2f-923d-cc90ee70ee7e"
      },
      "source": [
        "preparedData"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
              "\n",
              "\n",
              "<div id=\"ldavis_el1261399632394131048624942563\"></div>\n",
              "<script type=\"text/javascript\">\n",
              "\n",
              "var ldavis_el1261399632394131048624942563_data = {\"mdsDat\": {\"x\": [0.08151329777128036, -0.05460641362977084, 0.0786385970421109, -0.05136718012635428, 0.029236929437241183, -0.03402417914696262, -0.0493910513475447], \"y\": [-0.10115235549334013, 0.0058803192171902555, 0.06457159985881336, 0.0024597405105669904, 0.0679211026685063, -0.021524638215826536, -0.01815576854591018], \"topics\": [1, 2, 3, 4, 5, 6, 7], \"cluster\": [1, 1, 1, 1, 1, 1, 1], \"Freq\": [31.104831422058332, 22.815759109855406, 12.15378317109826, 11.527925511844012, 8.532324642590474, 7.010843374031815, 6.854532768521704]}, \"tinfo\": {\"Term\": [\"error\", \"tweet\", \"api\", \"json\", \"output\", \"plot\", \"app\", \"object\", \"map\", \"text\", \"country\", \"column\", \"component\", \"search\", \"date\", \"bar\", \"number\", \"word\", \"infected\", \"total\", \"case\", \"heroku\", \"button\", \"article\", \"death\", \"graph\", \"csv\", \"page\", \"row\", \"model\", \"csse\", 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            "text/plain": [
              "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
              "topic                                                \n",
              "1      0.081513 -0.101152       1        1  31.104831\n",
              "0     -0.054606  0.005880       2        1  22.815759\n",
              "3      0.078639  0.064572       3        1  12.153783\n",
              "6     -0.051367  0.002460       4        1  11.527926\n",
              "2      0.029237  0.067921       5        1   8.532325\n",
              "4     -0.034024 -0.021525       6        1   7.010843\n",
              "5     -0.049391 -0.018156       7        1   6.854533, topic_info=         Term       Freq      Total Category  logprob  loglift\n",
              "333     error  39.000000  39.000000  Default  30.0000  30.0000\n",
              "1046    tweet   8.000000   8.000000  Default  29.0000  29.0000\n",
              "35        api  24.000000  24.000000  Default  28.0000  28.0000\n",
              "549      json  22.000000  22.000000  Default  27.0000  27.0000\n",
              "690    output  17.000000  17.000000  Default  26.0000  26.0000\n",
              "...       ...        ...        ...      ...      ...      ...\n",
              "126      case   1.451919  32.340476   Topic7  -5.3371  -0.4232\n",
              "116    button   1.278348  11.393630   Topic7  -5.4644   0.4928\n",
              "761   problem   1.329172  19.347723   Topic7  -5.4254   0.0022\n",
              "35        api   1.339370  24.759504   Topic7  -5.4178  -0.2368\n",
              "788    python   1.255489  16.372234   Topic7  -5.4825   0.1122\n",
              "\n",
              "[419 rows x 6 columns], token_table=      Topic      Freq         Term\n",
              "term                              \n",
              "6         1  0.254707      achieve\n",
              "6         7  0.254707      achieve\n",
              "19        5  0.598875       affect\n",
              "19        6  0.598875       affect\n",
              "20        3  0.501265  afghanistan\n",
              "...     ...       ...          ...\n",
              "1109      3  0.285657        world\n",
              "1109      5  0.285657        world\n",
              "1111      3  0.626184    worldwide\n",
              "1126      4  0.459323         zoom\n",
              "1126      7  0.459323         zoom\n",
              "\n",
              "[766 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[2, 1, 4, 7, 3, 5, 6])"
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          },
          "metadata": {
            "tags": []
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          "execution_count": 118
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    {
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        {
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              "      <td>60548604</td>\n",
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              "      <td>searching trend dataset csv api format predict...</td>\n",
              "      <td>2</td>\n",
              "      <td>0.033920</td>\n",
              "      <td>0.796676</td>\n",
              "      <td>0.033870</td>\n",
              "      <td>0.033890</td>\n",
              "      <td>0.033905</td>\n",
              "      <td>0.033848</td>\n",
              "      <td>0.033891</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>61462376</td>\n",
              "      <td>&lt;p&gt;The for loop does not work if I use the $DA...</td>\n",
              "      <td>loop work daysdiff variable work number day da...</td>\n",
              "      <td>3</td>\n",
              "      <td>0.038517</td>\n",
              "      <td>0.038528</td>\n",
              "      <td>0.150813</td>\n",
              "      <td>0.475411</td>\n",
              "      <td>0.219770</td>\n",
              "      <td>0.038496</td>\n",
              "      <td>0.038466</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>61639535</td>\n",
              "      <td>&lt;p&gt;I'm trying to make a bubble plot of deaths ...</td>\n",
              "      <td>make bubble plot death shiny forgive obvious c...</td>\n",
              "      <td>4</td>\n",
              "      <td>0.796673</td>\n",
              "      <td>0.033971</td>\n",
              "      <td>0.033856</td>\n",
              "      <td>0.033980</td>\n",
              "      <td>0.033866</td>\n",
              "      <td>0.033833</td>\n",
              "      <td>0.033821</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1184</th>\n",
              "      <td>61710311</td>\n",
              "      <td>&lt;p&gt;This is my very first post on stackoverflow...</td>\n",
              "      <td>post stackoverflow explain struggle choropleth...</td>\n",
              "      <td>1184</td>\n",
              "      <td>0.766662</td>\n",
              "      <td>0.086674</td>\n",
              "      <td>0.029264</td>\n",
              "      <td>0.029332</td>\n",
              "      <td>0.029318</td>\n",
              "      <td>0.029512</td>\n",
              "      <td>0.029238</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1185</th>\n",
              "      <td>61710868</td>\n",
              "      <td>&lt;p&gt;I am trying to write a script which will cy...</td>\n",
              "      <td>write script cycle worksheet workbook delete w...</td>\n",
              "      <td>1185</td>\n",
              "      <td>0.806857</td>\n",
              "      <td>0.032415</td>\n",
              "      <td>0.031947</td>\n",
              "      <td>0.032124</td>\n",
              "      <td>0.032011</td>\n",
              "      <td>0.031973</td>\n",
              "      <td>0.032672</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1186</th>\n",
              "      <td>61711506</td>\n",
              "      <td>&lt;p&gt;I tried to create an interactive choropleth...</td>\n",
              "      <td>create interactive choropleth plot confirmed c...</td>\n",
              "      <td>1186</td>\n",
              "      <td>0.814190</td>\n",
              "      <td>0.031096</td>\n",
              "      <td>0.030955</td>\n",
              "      <td>0.030988</td>\n",
              "      <td>0.030934</td>\n",
              "      <td>0.030905</td>\n",
              "      <td>0.030933</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1187</th>\n",
              "      <td>61711600</td>\n",
              "      <td>&lt;p&gt;I am learning flutter and trying to parse a...</td>\n",
              "      <td>learning flutter parse json article error lib ...</td>\n",
              "      <td>1187</td>\n",
              "      <td>0.412457</td>\n",
              "      <td>0.034212</td>\n",
              "      <td>0.034388</td>\n",
              "      <td>0.034142</td>\n",
              "      <td>0.034235</td>\n",
              "      <td>0.416096</td>\n",
              "      <td>0.034470</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1188</th>\n",
              "      <td>61713102</td>\n",
              "      <td>&lt;p&gt;I am downloding some json and merging them,...</td>\n",
              "      <td>downloding json merging creates simply replace...</td>\n",
              "      <td>1188</td>\n",
              "      <td>0.036956</td>\n",
              "      <td>0.037087</td>\n",
              "      <td>0.036792</td>\n",
              "      <td>0.036993</td>\n",
              "      <td>0.778376</td>\n",
              "      <td>0.036824</td>\n",
              "      <td>0.036971</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>1189 rows × 12 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "            Id  ... topic\n",
              "0     60548604  ...     2\n",
              "1     60635909  ...     2\n",
              "2        72695  ...     2\n",
              "3     61462376  ...     4\n",
              "4     61639535  ...     1\n",
              "...        ...  ...   ...\n",
              "1184  61710311  ...     1\n",
              "1185  61710868  ...     1\n",
              "1186  61711506  ...     1\n",
              "1187  61711600  ...     6\n",
              "1188  61713102  ...     5\n",
              "\n",
              "[1189 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mZ0lBCGxaqV8",
        "colab_type": "text"
      },
      "source": [
        "# GitHub"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "29uKqzI5Co9a",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 763
        },
        "outputId": "da30b5db-9087-43a0-e296-5ca245c10289"
      },
      "source": [
        "number_topics = 11\n",
        "column_text = 'description'\n",
        "column_text_processed = 'description_processed'\n",
        "is_html = False\n",
        "with_tfidf = True\n",
        "check_language = True\n",
        "\n",
        "# Load dataframe\n",
        "df2 = pd.read_csv('/content/drive/My Drive/covid-es-shared/report/20200604_COVID19_Github_Data.csv', sep=';', quoting=csv.QUOTE_ALL)\n",
        "# df2 = df2.drop(columns=['Unnamed: 0'], axis=1)\n",
        "\n",
        "preparedData, df2 = generate_topics(df2, column_text, column_text_processed, is_html, number_topics, with_tfidf, check_language=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Load dataframe shape:  (61611, 18)\n",
            "After removing invalid values: (38267, 18)\n",
            "After removing blank values in text processing:  (36889, 19)\n",
            "After removing unwritten text in English: (27284, 20)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/feature_extraction/text.py:1817: UserWarning: Only (<class 'numpy.float64'>, <class 'numpy.float32'>, <class 'numpy.float16'>) 'dtype' should be used. <class 'numpy.int64'> 'dtype' will be converted to np.float64.\n",
            "  UserWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x667.491 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vVufHe8IGtnj",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 881
        },
        "outputId": "96d38596-dd24-463b-b5a2-ff7cba59823b"
      },
      "source": [
        "preparedData"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "<link rel=\"stylesheet\" type=\"text/css\" href=\"https://cdn.rawgit.com/bmabey/pyLDAvis/files/ldavis.v1.0.0.css\">\n",
              "\n",
              "\n",
              "<div id=\"ldavis_el1261399633215535683057946778\"></div>\n",
              "<script type=\"text/javascript\">\n",
              "\n",
              "var ldavis_el1261399633215535683057946778_data = {\"mdsDat\": {\"x\": [-0.22917466318684104, -0.10854472934199785, 0.06713757791027057, 0.03274582143488959, 0.14815829013036122, 0.2135187709556703, -0.07339207800967282, 0.1385725511839454, 0.01535310822117419, -0.06088791673875353, -0.14348673255904545], \"y\": [0.0016630651786448867, 0.11110794020596679, 0.2488857031332981, 0.04327325452299246, -0.06971153248170138, 0.003142524313906581, 0.07975806422062677, -0.05668121831518534, -0.19100514074119648, -0.046515995455507574, -0.12391666458184493], \"topics\": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11], \"cluster\": [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], \"Freq\": [14.672073160004743, 10.651677003034653, 10.23934923322417, 9.011102274550248, 8.74339589757134, 8.044495990665826, 7.922919178191634, 7.866644834216162, 7.754788033736383, 7.620943553890204, 7.472610840914642]}, \"tinfo\": {\"Term\": [\"tracker\", \"analysis\", \"react\", \"bot\", \"stats\", \"sars\", \"cov\", \"death\", 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            "text/plain": [
              "PreparedData(topic_coordinates=              x         y  topics  cluster       Freq\n",
              "topic                                                \n",
              "0     -0.229175  0.001663       1        1  14.672073\n",
              "10    -0.108545  0.111108       2        1  10.651677\n",
              "9      0.067138  0.248886       3        1  10.239349\n",
              "2      0.032746  0.043273       4        1   9.011102\n",
              "6      0.148158 -0.069712       5        1   8.743396\n",
              "1      0.213519  0.003143       6        1   8.044496\n",
              "5     -0.073392  0.079758       7        1   7.922919\n",
              "3      0.138573 -0.056681       8        1   7.866645\n",
              "8      0.015353 -0.191005       9        1   7.754788\n",
              "7     -0.060888 -0.046516      10        1   7.620944\n",
              "4     -0.143487 -0.123917      11        1   7.472611, topic_info=          Term        Freq        Total Category  logprob  loglift\n",
              "2843   tracker  774.000000   774.000000  Default  30.0000  30.0000\n",
              "103   analysis  643.000000   643.000000  Default  29.0000  29.0000\n",
              "2258     react  379.000000   379.000000  Default  28.0000  28.0000\n",
              "298        bot  201.000000   201.000000  Default  27.0000  27.0000\n",
              "2664     stats  288.000000   288.000000  Default  26.0000  26.0000\n",
              "...        ...         ...          ...      ...      ...      ...\n",
              "3032   website   32.016200   370.295141  Topic11  -4.8770   0.1459\n",
              "2928      user   23.786895    98.053718  Topic11  -5.1742   1.1775\n",
              "688       data   28.154434  1129.091943  Topic11  -5.0056  -1.0975\n",
              "2608    source   23.315640   176.880849  Topic11  -5.1942   0.5676\n",
              "1589      live   23.037563   237.017449  Topic11  -5.2062   0.2629\n",
              "\n",
              "[687 rows x 6 columns], token_table=      Topic      Freq      Term\n",
              "term                           \n",
              "22        2  0.964241    active\n",
              "32       11  0.816683  addition\n",
              "44        9  0.942171  advanced\n",
              "51        1  0.226548  affected\n",
              "51        2  0.693804  affected\n",
              "...     ...       ...       ...\n",
              "3082      3  0.884781     wuhan\n",
              "3082      6  0.073732     wuhan\n",
              "3082      7  0.024577     wuhan\n",
              "3083      2  0.944359   xamarin\n",
              "3086      5  0.941746      xray\n",
              "\n",
              "[1673 rows x 3 columns], R=30, lambda_step=0.01, plot_opts={'xlab': 'PC1', 'ylab': 'PC2'}, topic_order=[1, 11, 10, 3, 7, 2, 6, 4, 9, 8, 5])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 121
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xPrvL_DuF7vv",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "11c4cc2e-b121-4ba6-c1a2-2084aea2b721"
      },
      "source": [
        "df = pd.read_csv('/content/drive/My Drive/covid-es-shared/report/20200604_COVID19_Github_Data.csv', sep=';', quoting=csv.QUOTE_ALL)\n",
        "df = df.drop(columns=['Unnamed: 0'], axis=1)\n",
        "df['ID'] = range(df.shape[0])\n",
        "\n",
        "df2 = df2.drop(columns=['name', 'nameWithOwner', 'description', 'createdAt',\n",
        "       'updatedAt', 'pushedAt', 'forkCount', 'diskUsage', 'owner.login',\n",
        "       'primaryLanguage.name', 'pullRequests.totalCount', 'issues.totalCount',\n",
        "       'mentionableUsers.totalCount', 'stargazers.totalCount',\n",
        "       'watchers.totalCount', 'owner.location', 'primaryLanguage'], axis=1)\n",
        "\n",
        "\n",
        "df.set_index('ID')\n",
        "df2.set_index('ID')\n",
        "\n",
        "\n",
        "result = pd.merge(df, df2, how='left', on='ID', validate=\"one_to_one\")\n",
        "print(result.shape)\n",
        "result = result.fillna(0)\n",
        "print(result.shape)\n",
        "result"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(61611, 32)\n",
            "(61611, 32)\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
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              "      <th></th>\n",
              "      <th>name</th>\n",
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              "      <th>createdAt</th>\n",
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              "      <th>primaryLanguage.name</th>\n",
              "      <th>pullRequests.totalCount</th>\n",
              "      <th>issues.totalCount</th>\n",
              "      <th>mentionableUsers.totalCount</th>\n",
              "      <th>stargazers.totalCount</th>\n",
              "      <th>watchers.totalCount</th>\n",
              "      <th>owner.location</th>\n",
              "      <th>primaryLanguage</th>\n",
              "      <th>ID</th>\n",
              "      <th>Unnamed: 0</th>\n",
              "      <th>description_processed</th>\n",
              "      <th>topic_1</th>\n",
              "      <th>topic_2</th>\n",
              "      <th>topic_3</th>\n",
              "      <th>topic_4</th>\n",
              "      <th>topic_5</th>\n",
              "      <th>topic_6</th>\n",
              "      <th>topic_7</th>\n",
              "      <th>topic_8</th>\n",
              "      <th>topic_9</th>\n",
              "      <th>topic_10</th>\n",
              "      <th>topic_11</th>\n",
              "      <th>topic</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>FNAEG_back</td>\n",
              "      <td>victordrnd/FNAEG_back</td>\n",
              "      <td>Plateforme de gestion d'inventaires et de comm...</td>\n",
              "      <td>2019-12-02T15:37:21Z</td>\n",
              "      <td>2020-05-20T15:11:21Z</td>\n",
              "      <td>2020-05-01T14:21:58Z</td>\n",
              "      <td>0</td>\n",
              "      <td>272</td>\n",
              "      <td>victordrnd</td>\n",
              "      <td>PHP</td>\n",
              "      <td>9</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>QA-for-Government-Affairs-about-Coronavirus-Po...</td>\n",
              "      <td>VincentGaoHJ/QA-for-Government-Affairs-about-C...</td>\n",
              "      <td>Endeavour to make full use of hierarchical inf...</td>\n",
              "      <td>2019-12-08T07:25:45Z</td>\n",
              "      <td>2020-03-25T12:22:07Z</td>\n",
              "      <td>2020-03-25T12:22:04Z</td>\n",
              "      <td>0</td>\n",
              "      <td>15968</td>\n",
              "      <td>VincentGaoHJ</td>\n",
              "      <td>Python</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1</td>\n",
              "      <td>1.0</td>\n",
              "      <td>endeavour make full hierarchical information e...</td>\n",
              "      <td>0.025258</td>\n",
              "      <td>0.025258</td>\n",
              "      <td>0.025266</td>\n",
              "      <td>0.315755</td>\n",
              "      <td>0.025259</td>\n",
              "      <td>0.025258</td>\n",
              "      <td>0.025258</td>\n",
              "      <td>0.025260</td>\n",
              "      <td>0.025258</td>\n",
              "      <td>0.025257</td>\n",
              "      <td>0.456913</td>\n",
              "      <td>11.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>covid-sms-stats</td>\n",
              "      <td>vivkpatl/covid-sms-stats</td>\n",
              "      <td>An exercise in recycling someone else's data t...</td>\n",
              "      <td>2019-12-14T07:26:17Z</td>\n",
              "      <td>2020-03-26T05:02:10Z</td>\n",
              "      <td>2020-04-05T14:56:08Z</td>\n",
              "      <td>0</td>\n",
              "      <td>201</td>\n",
              "      <td>vivkpatl</td>\n",
              "      <td>JavaScript</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>4</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2</td>\n",
              "      <td>2.0</td>\n",
              "      <td>exercise recycling data make useless app fresh...</td>\n",
              "      <td>0.027732</td>\n",
              "      <td>0.027729</td>\n",
              "      <td>0.027735</td>\n",
              "      <td>0.027729</td>\n",
              "      <td>0.027730</td>\n",
              "      <td>0.027728</td>\n",
              "      <td>0.027729</td>\n",
              "      <td>0.027733</td>\n",
              "      <td>0.722695</td>\n",
              "      <td>0.027730</td>\n",
              "      <td>0.027729</td>\n",
              "      <td>9.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>covid-app</td>\n",
              "      <td>kiriac/covid-app</td>\n",
              "      <td>0</td>\n",
              "      <td>2019-12-17T15:16:08Z</td>\n",
              "      <td>2020-04-06T17:44:10Z</td>\n",
              "      <td>2020-04-06T17:29:26Z</td>\n",
              "      <td>0</td>\n",
              "      <td>760</td>\n",
              "      <td>kiriac</td>\n",
              "      <td>TypeScript</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3</td>\n",
              "      <td>4.0</td>\n",
              "      <td>azure data factory show trend case death unemp...</td>\n",
              "      <td>0.026207</td>\n",
              "      <td>0.026207</td>\n",
              "      <td>0.188093</td>\n",
              "      <td>0.026206</td>\n",
              "      <td>0.026210</td>\n",
              "      <td>0.026207</td>\n",
              "      <td>0.026206</td>\n",
              "      <td>0.190759</td>\n",
              "      <td>0.026208</td>\n",
              "      <td>0.411482</td>\n",
              "      <td>0.026215</td>\n",
              "      <td>10.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>DataFactoryCovid19</td>\n",
              "      <td>timothyjpauley/DataFactoryCovid19</td>\n",
              "      <td>This is a project in Azure Data Factory to sho...</td>\n",
              "      <td>2019-12-21T06:22:20Z</td>\n",
              "      <td>2020-05-29T02:12:05Z</td>\n",
              "      <td>2020-05-29T02:12:03Z</td>\n",
              "      <td>0</td>\n",
              "      <td>16714</td>\n",
              "      <td>timothyjpauley</td>\n",
              "      <td>TSQL</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61606</th>\n",
              "      <td>Proses-Thread-Senkronizasyonu</td>\n",
              "      <td>alperdem1rel/Proses-Thread-Senkronizasyonu</td>\n",
              "      <td>Sağlık Çalışanları Giyinme Simülasyonu (Covid-...</td>\n",
              "      <td>2020-05-12T21:28:49Z</td>\n",
              "      <td>2020-05-12T23:13:34Z</td>\n",
              "      <td>2020-05-12T23:13:32Z</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>alperdem1rel</td>\n",
              "      <td>C</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>61606</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61607</th>\n",
              "      <td>corona-skill</td>\n",
              "      <td>victoraldir/corona-skill</td>\n",
              "      <td>Skill that informs the latest rate of COVID-19...</td>\n",
              "      <td>2020-05-12T01:06:14Z</td>\n",
              "      <td>2020-05-12T02:20:49Z</td>\n",
              "      <td>2020-05-12T01:09:14Z</td>\n",
              "      <td>0</td>\n",
              "      <td>55</td>\n",
              "      <td>victoraldir</td>\n",
              "      <td>Python</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>61607</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61608</th>\n",
              "      <td>Deep-Learning-COVID-19-on-CXR-using-Limited-Tr...</td>\n",
              "      <td>bispl-kaist/Deep-Learning-COVID-19-on-CXR-usin...</td>\n",
              "      <td>Github repository for Deep Learning COVID-19 o...</td>\n",
              "      <td>2020-05-12T07:45:25Z</td>\n",
              "      <td>2020-05-12T13:18:54Z</td>\n",
              "      <td>2020-05-12T22:01:56Z</td>\n",
              "      <td>0</td>\n",
              "      <td>102</td>\n",
              "      <td>bispl-kaist</td>\n",
              "      <td>Python</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>61608</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61609</th>\n",
              "      <td>SFEmpComp</td>\n",
              "      <td>AbhishekR3/SFEmpComp</td>\n",
              "      <td>An analysis of the effects of Covid-19 on jobs...</td>\n",
              "      <td>2020-05-12T10:36:04Z</td>\n",
              "      <td>2020-05-13T06:09:51Z</td>\n",
              "      <td>2020-05-13T06:09:48Z</td>\n",
              "      <td>0</td>\n",
              "      <td>1061</td>\n",
              "      <td>AbhishekR3</td>\n",
              "      <td>Python</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>61609</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61610</th>\n",
              "      <td>Aviation-Searching-Algorithm</td>\n",
              "      <td>Owen-Darkhorse/Aviation-Searching-Algorithm</td>\n",
              "      <td>During COVID-19 pandemic, countries around the...</td>\n",
              "      <td>2020-05-12T00:47:04Z</td>\n",
              "      <td>2020-05-14T22:48:01Z</td>\n",
              "      <td>2020-05-14T22:47:59Z</td>\n",
              "      <td>0</td>\n",
              "      <td>5</td>\n",
              "      <td>Owen-Darkhorse</td>\n",
              "      <td>Python</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>61610</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "      <td>0.000000</td>\n",
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              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>61611 rows × 32 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                    name  ... topic\n",
              "0                                             FNAEG_back  ...   0.0\n",
              "1      QA-for-Government-Affairs-about-Coronavirus-Po...  ...  11.0\n",
              "2                                        covid-sms-stats  ...   9.0\n",
              "3                                              covid-app  ...  10.0\n",
              "4                                     DataFactoryCovid19  ...   0.0\n",
              "...                                                  ...  ...   ...\n",
              "61606                      Proses-Thread-Senkronizasyonu  ...   0.0\n",
              "61607                                       corona-skill  ...   0.0\n",
              "61608  Deep-Learning-COVID-19-on-CXR-using-Limited-Tr...  ...   0.0\n",
              "61609                                          SFEmpComp  ...   0.0\n",
              "61610                       Aviation-Searching-Algorithm  ...   0.0\n",
              "\n",
              "[61611 rows x 32 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 122
        }
      ]
    }
  ]
}